code: def simulate_data(
      m,
      n,
      k=2,
      n_informative="auto",
      random_state=None,
      classification=True,
      bias=0.0,
  ):
      if n_informative == "auto":
          n_informative = n // 5
      if classification:
          features, labels = make_classification(
              n_samples=m,
              n_features=n,
              n_informative=n_informative,
              n_redundant=n - n_informative,
              n_classes=k,
              random_state=random_state,
          )
      else:
          features, labels = make_regression(
              n_samples=m,
              n_features=n,
              n_informative=n_informative,
              n_targets=1,
              bias=bias,
              random_state=random_state,
          )
      return (
          np.c_[features].astype(np.float32),
          np.c_[labels].astype(np.float32).flatten(),
      )
  
imports: import numpy
  import pytest
  import os
  import pandas
  from math import ceil
  from cuml.experimental import ForestInference
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml.internals.import_utils import has_lightgbm, has_xgboost
  from cuml.common.device_selection import using_device_type
  from sklearn.datasets import make_classification, make_regression
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.model_selection import train_test_split
  import treelite
label_desc: Use image node_id 1 for calling a global function with example usage: simulate_data(m, n, k, n_informative, random_state, classification, bias) and returns: np,  np